{"title":"基于广义命令响应模型和分数条件变分自编码器的歌唱基频轮廓生成","authors":"Shogo Seki, Haruka Taga, T. Toda","doi":"10.1109/mlsp52302.2021.9596428","DOIUrl":null,"url":null,"abstract":"This paper proposes a method for achieving physically motivated and interpretable control of fundamental frequency (F0) contour generation in singing aid systems for laryngectomees. Recently proposed variational autoencoder (VAE)-based method, VAE-SPACE, has successfully generated singing F0 contours from musical scores. However, VAE-SPACE can generate physically deviated F0 contours. Moreover, to represent fluctuations in F0 contours, VAE-SPACE requires manual adjustment of noise components used as the input with musical scores. To address these issues, the proposed method 1) introduces a generalized command-response (GCR) model to represent an F0 contour as an approximation of a physical F0 production mechanism, and 2) employs a conditional VAE (CVAE) to treat musical scores and the noise components separately. The experimental results reveal that the proposed method achieves comparable performance as VAE-SPACE without the manual adjustment of noise components and makes it possible to control F0 contours more intuitively by using the trained GCR model.","PeriodicalId":156116,"journal":{"name":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Singing Fundamental Frequency Contour Generation Using Generalized Command-Response Model and Score-Conditional Variational Autoencoder\",\"authors\":\"Shogo Seki, Haruka Taga, T. Toda\",\"doi\":\"10.1109/mlsp52302.2021.9596428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a method for achieving physically motivated and interpretable control of fundamental frequency (F0) contour generation in singing aid systems for laryngectomees. Recently proposed variational autoencoder (VAE)-based method, VAE-SPACE, has successfully generated singing F0 contours from musical scores. However, VAE-SPACE can generate physically deviated F0 contours. Moreover, to represent fluctuations in F0 contours, VAE-SPACE requires manual adjustment of noise components used as the input with musical scores. To address these issues, the proposed method 1) introduces a generalized command-response (GCR) model to represent an F0 contour as an approximation of a physical F0 production mechanism, and 2) employs a conditional VAE (CVAE) to treat musical scores and the noise components separately. The experimental results reveal that the proposed method achieves comparable performance as VAE-SPACE without the manual adjustment of noise components and makes it possible to control F0 contours more intuitively by using the trained GCR model.\",\"PeriodicalId\":156116,\"journal\":{\"name\":\"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/mlsp52302.2021.9596428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/mlsp52302.2021.9596428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Singing Fundamental Frequency Contour Generation Using Generalized Command-Response Model and Score-Conditional Variational Autoencoder
This paper proposes a method for achieving physically motivated and interpretable control of fundamental frequency (F0) contour generation in singing aid systems for laryngectomees. Recently proposed variational autoencoder (VAE)-based method, VAE-SPACE, has successfully generated singing F0 contours from musical scores. However, VAE-SPACE can generate physically deviated F0 contours. Moreover, to represent fluctuations in F0 contours, VAE-SPACE requires manual adjustment of noise components used as the input with musical scores. To address these issues, the proposed method 1) introduces a generalized command-response (GCR) model to represent an F0 contour as an approximation of a physical F0 production mechanism, and 2) employs a conditional VAE (CVAE) to treat musical scores and the noise components separately. The experimental results reveal that the proposed method achieves comparable performance as VAE-SPACE without the manual adjustment of noise components and makes it possible to control F0 contours more intuitively by using the trained GCR model.